Latent Dirichlet Allocation

7.3 Collaborative filtering

1. How was LDA applied to collaborative filtering?
   Users are documents and their preferred movies are words (EachMovie). Shown all but one of a test user’s movies, the model predicts the held-out one: \(p(w \mid \mathbf{w}_{\text{obs}}) = \int \sum_z p(w \mid z)\, p(z \mid \theta)\, p(\theta \mid \mathbf{w}_{\text{obs}})\, d\theta\). LDA had the best predictive perplexity.
   • Blei et al., 2003, Latent Dirichlet Allocation (Journal of Machine Learning Research), §7.3, p. 1014